Enhancing the Spatio-Temporal Observability of Residential Loads

TL;DR

This paper develops a convex optimization framework that combines low-resolution smart meter data and high-rate synchrophasor data to improve the observability of residential loads, aiding in efficient distribution system management.

Contribution

It introduces a novel joint inference method leveraging regularization for low rank and sparsity, enhancing load recovery accuracy with limited observations.

Findings

Effective appliance activity identification

Accurate PV output profile recovery

Improved load estimation performance

Abstract

Enhancing the spatio-temporal observability of residential loads is crucial for achieving secure and efficient operations in distribution systems with increasing penetration of distributed energy resources (DERs). This paper presents a joint inference framework for residential loads by leveraging the real-time measurements from distribution-level sensors. Specifically, smart meter data is available for almost every load with unfortunately low temporal resolution, while distribution synchrophasor data is at very fast rates yet available at limited locations. By combining these two types of data with respective strengths, the problem is cast as a matrix recovery one with much less number of observations than unknowns. To improve the recovery performance, we introduce two regularization terms to promote a low rank plus sparse structure of the load matrix via a difference transformation. Accordingly, the recovery problem can be formulated as a convex optimization one which is efficiently solvable. Numerical tests using real residential load data demonstrate the effectiveness of our proposed approaches in identifying appliance activities and recovering the PV output profiles.